Thursday, 31 August 2023
Boundary Waters (Hyatt Regency Minneapolis)
The Spectral Bin Model (SBM) is a 1-dimensional Lagrangian microphysics model used to diagnose winter precipitation types. The SBM determines the 7 precipitation types (RA [rain], SN [snow], RASN, IP [ice pellet], FZRA [freezing rain], FZDZ [freezing drizzle], FZRAIP) through the explicit calculation of precipitation process at given background environmental condition. The performance of the SBM can be influenced by errors in the representation of background environmental conditions. A previous case study showed that the integrated use of the SBM and High-Resolution Rapid Refresh (HRRR) model mis-predicted the ground precipitation type as FZRA, whereas the integrated use of the SBM and observation data resulted in accurate prediction of the type (IP) due to a positive Tw (wet-bulb temperature) bias at the refreezing layer in the HRRR model.
In this study, we aim to incorporate a numerical model into the SBM to diagnose the winter precipitation type in Pyeongchang area, a complex mountainous region in South Korea. We use the analysis field of the Local Data Assimilation and Prediction System (LDAPS) as input environmental profiles for the SBM. The LDAPS is a numerical model with a 1.5 km horizontal grid spacing developed by KMA (Korea Meteorological Administration). The errors in LDAPS are explored by comparing sounding data collected extensively at multiple sites during the field campaign. LDAPS profiles show relatively low (high) humidity and Tw than sounding data at higher (lower) than 4 km altitude for low-level high-humidity events of the 2017-2018 winter season in Pyeongchang. The simulation results of 131 precipitation events consisting of RA, RASN, and SN are compared between the SBM integrated with LDAPS (SBML) and the SBM integrated with sounding (SBMS). The hit rate (80.2%) of SBML is relatively lower than that (90.1%) of SBMS. We will explore better ways to incorporate LDAPS profiles to improve the accuracy of the SBM.
Acknowledgment This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740.
Key words
Winter precipitation type, Spectral Bin Model, LDAPS, HRRR
In this study, we aim to incorporate a numerical model into the SBM to diagnose the winter precipitation type in Pyeongchang area, a complex mountainous region in South Korea. We use the analysis field of the Local Data Assimilation and Prediction System (LDAPS) as input environmental profiles for the SBM. The LDAPS is a numerical model with a 1.5 km horizontal grid spacing developed by KMA (Korea Meteorological Administration). The errors in LDAPS are explored by comparing sounding data collected extensively at multiple sites during the field campaign. LDAPS profiles show relatively low (high) humidity and Tw than sounding data at higher (lower) than 4 km altitude for low-level high-humidity events of the 2017-2018 winter season in Pyeongchang. The simulation results of 131 precipitation events consisting of RA, RASN, and SN are compared between the SBM integrated with LDAPS (SBML) and the SBM integrated with sounding (SBMS). The hit rate (80.2%) of SBML is relatively lower than that (90.1%) of SBMS. We will explore better ways to incorporate LDAPS profiles to improve the accuracy of the SBM.
Acknowledgment This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740.
Key words
Winter precipitation type, Spectral Bin Model, LDAPS, HRRR
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